In order to evaluate the capability of building damage detection from optical satellite images, a procedure for digital image analysis is examined and applied to images captured before and after the 2006 Central Java, Indonesia, earthquake. In the image analysis, the pixels of the images are classified into vegetation, bare ground, and built-up areas. The damage areas are detected by the differential of the digital numbers in the built-up areas. The estimated damage distribution is validated by comparing it with the GIS data on building damage obtained from a field survey. The results show that the severely damaged areas were well detected by the analysis. In the densely vegetated areas, however, the damage was underestimated because many of the buildings were obscured by trees. For assessing quantitative damage information, the relationship between the number of collapsed buildings and the areas detected by the image analysis is evaluated.
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References
1.
Al-KhudhairyD. H. A., CaravaggiI., and GiadaS., 2005. Structural damage assessments from Ikonos data using change detection, object-oriented segmentation, and classification techniques, Photogrammetric Engineering & Remote Sensing71, 825–837.
2.
Architectural Institute of Japan, 2007. Report on the Damage Investigation of the 2006 Central Java Earthquake, 296 pp.
3.
ArciniegasG. A., BijikerW., KerleN., and TolpekinV. A., 2007. Coherence- and amplitude-based analysis of seismogenic damage in Bam, Iran, using ENVISAT ASAR data, IEEE Trans. on Geoscience and Remote Sensing45, 1571–1581.
BlaschkeT., 2004. Towards a framework for change detection based on image objects, Proceedings of 1st Goettingen GIS and Remote Sensing Days (GGRS), Paper No. G001.
6.
BoothE., SaitoK., SpenceR., MadabhushiG., and EguchiR. T., 2011. Validating assessments of seismic damage made from remote sensing, Earthquake Spectra27, S157–S177.
7.
GambaP., Dell'AcquaF., and TrianniG., 2007. Rapid damage detection in the Bam area using multitemporal SAR and exploiting ancillary data, IEEE Trans. on Geoscience and Remote Sensing45, 1582–1590.
8.
GusellaL., AdamsB., BitelliG., HuyckC., and MognolA., 2005. Object-oriented understanding and post-earthquake damage assessment for the 2003 Bam, Iran, earthquake, Earthquake Spectra21, S225–S238.
9.
HaralickR. M., ShanmugamK., and DinsteinI., 1973. Textural features for image classification, IEEE Trans. Systems, Man and Cybernetics3, 610–621.
10.
HuyckC. K., AdamsB. J., ChoS., ChungH. C., and EguchiR. T., 2005. Towards rapid citywide damage mapping using neighborhood edge dissimilarities in very high-resolution optical satellite imagery—application to the 2003 Bam, Iran, earthquake, Earthquake Spectra21, S255–S266.
KarnawatiD., PramumijoyoS., AndersonR., and HuseinS. (eds.), 2008. The Yogyakarta Earthquake of May 27, 2006, Star Publishing Company, Inc.
14.
KerleN., 2010. Satellite-based damage mapping following the 2006 Indonesia earthquake:How accurate was it?, International Journal of Applied Earth Observation and Geoinformation12, 466–476.
15.
KohiyamaM., and YamazakiF., 2005. Image fluctuation model for damage detection using middle-resolution satellite imagery, International Journal of Remote Sensing26, 5603–5627.
16.
LuD., MauselP., BrondizioE., and MoranE., 2004. Change detection techniques, International Journal of Remote Sensing25, 2365–2407.
17.
LunettaR. S., and ElvidgeC. D. (eds.), 1998. Remote Sensing Change Detection:Environmental Monitoring Methods and Applications, Ann Arbor Press, 318 pp.
18.
MatsuokaM., and YamazakiF., 2004. Use of satellite SAR intensity imagery for detecting building areas damaged due to earthquakes, Earthquake Spectra20, 975–994.
19.
MiuraH., and MidorikawaS., 2010. Building damage distribution in the southern part of Beichuan County by the 2008 Wenchuan, China, earthquake detected from satellite optical images, Proceedings of 9th US National and 10th Canadian Conference on Earthquake Engineering, Paper No. 789.
20.
MiuraH., YamazakiF., and MatsuokaM., 2007. Identification of damaged areas due to the 2006 Central Java, Indonesia, earthquake using satellite optical images, Proceedings of 2007 Urban Remote Sensing Joint Event, Paper No. DIS4.
21.
RathjeE. M., and AdamsB. J., 2008. The role of remote sensing in earthquake science and engineering: Opportunities and challenges, Earthquake Spectra24, 471–492.
22.
RESPOND, 2006. Indonesia/Java—Damage assessment of the earthquake on May 27, 2006, http://www.respond-int.org.
23.
SaitoK., SpenceR. J. S., GoingC., and MarkusM., 2004. Using high-resolution satellite images for post-earthquake building damage assessment: A study following the 26 January 2001 Gujarat earthquake, Earthquake Spectra20, 145–169.
24.
SinghA., 1989. Digital change detection techniques using remotely-sensed data, International Journal of Remote Sensing10, 989–1003.
25.
StumpfA., and KerleN., 2011. Object-oriented mapping of landslides using random forests, Remote Sensing of Environment115, 2564–2577.
26.
TakagiM., and ShimodaH., 2004. Handbook of Image Analysis (Revised Version), University of Tokyo Press, 1991 pp. (in Japanese).
27.
TuckerC. J., 1979. Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment8, 127–150.
28.
VuT. T., MatsuokaM., and YamazakiF., 2007. Dual-scale approach for detection of tsunami-affected areas using optical satellite images, International Journal of Remote Sensing28, 2995–3011.
29.
UNITAR Operational Satellite Applications Programme (UNOSAT), 2006. Preliminary Damage Assessment—Java Earthquake, Product ID: 669, 1 June 2006, available at http://www.unitar.org/unosat/node/44/669.
30.
YamazakiF., YanoY., and MatsuokaM., 2005. Visual damage interpretation of buildings in Bam City using QuickBird images following the 2003 Bam, Iran, earthquake, Earthquake Spectra21, S329–S336.
